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There are many different algorithms that have been shown to solve the simultaneous localization and mapping (SLAM) problem depending on the type of input data. Many of these algorithms use some form of cumulative current position as a state variable and only store landmarks in their globally mapped form, discarding past data. This thesis takes a different approach in not using current position as a cumulative state variable and storing and using past data. Landmarks are mapped relative to each other in their untransformed states and use either three points or one plane to maintain translation and rotation invariance. The Relative algorithms can use both current and past data for accuracy purposes. Using this approach, the SLAM problem is solved by data structures and algorithms rather than probabilistic modeling. The Relative algorithms are shown to be good solutions to the simulated SLAM problems tested in this thesis. In particular the Relative Point algorithm is shown to have a worst case computation complexity of O(nslogns). ns is the average quantity of points observed in a given observation and is not related to the total quantity of points on the map. The Relative Point algorithm is able to identify points with movement that is not correlated to the viewpoint at a low cost, and has comparable accuracy to a 6D no odometry Extended Kalman Filter.

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Previous research on multimedia information filtering has mainly concentrated on key frame identification and video skim generation for browsing purposes, however applications requiring the generation of summaries as the final product for user con- sumption are of equal scientific and commercial interest. Recent advances in computer vision have enabled the extraction of semantic events from an audio-visual signal, so it can be assumed for our purposes that such semantic labels are already available for use. We concentrate instead on developing methods to prioritise these semantic elements for inclusion in a summary which can be personalised to meet a particular user's needs. Our work differentiates itself from that in the literature as it is driven by the results of a knowledge elicitation study with expert summarisers. The experts in our study believe that summaries structured as a narrative are better able to convey the content of the original data to a user. Motivated by the information filtering problem, the primary contribution of this thesis is the design and implementation of a system to summarise sequences of events by automatic modelling of the causal relationships between them. We show, by com- parison against summaries generated by experts and with the introduction of a new coherence metric, that modelling the causal relationships between events increases the coherence and accuracy of summaries. We suggest that this claim is valid, not only in the domain of soccer highlights generation, in which we carry out the bulk of our experiments, but also in any other domain in which causal relationships can be iden- tified between events. This proposal is tested by applying our summarisation system to another, significantly different domain, that of business meeting summarisation, using the soccer training set and a manually generated ontology mapping. We introduce the concept of a context-group of causally related events as a first step towards modelling narrative episodes and present a comparison between a case based reasoning and a two-stage Markov model approach to summarisation. For both methods we show that by including entire context-groups in the summary, rather than single events in isolation, more accurate summaries can be generated. Our approach to personalisation biases a summary according to particular narrative plotlines using different subsets of the training data. Results show that the number of instances of certain event classes can be increased by biasing the training set appropriately. This method gives very similar results to a standard weighting method, while avoiding the need to tailor the weights to a particular application domain.

The interest of using GPU:s as general processing units for heavy computations (GPGPU) has increased in the last couple of years. Manufacturers such as Nvidia and AMD make GPU:s powerful enough to outrun CPU:s in one order of magnitude, for suitable algorithms. For embedded systems, GPU:s are not as popular yet. The embedded GPU:s available on the market have often not been able to justify hardware changes from the current systems (CPU:s and FPGA:s) to systems using embedded GPU:s. They have been too hard to get, too energy consuming and not suitable for some algorithms. At SICK IVP, advanced computer vision algorithms run on FPGA:s. This master thesis optimizes two such algorithms for embedded GPU:s and evaluates the result. It also evaluates the status of the embedded GPU:s on the market today. The results indicates that embedded GPU:s perform well enough to run the evaluatedd algorithms as fast as needed. The implementations are also easy to understand compared to implementations for FPGA:s which are competing hardware.

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By the availability of today&rsquo / s inexpensive powerful hardware, it becomes possible to design real-time computer vision systems even in personal computers. Therefore, computer vision becomes a powerful tool for human-computer interaction (HCI). In this study, three different vision-based HCI systems are described. As in all vision-based HCI systems, the developed systems requires a camera (a webcam) to monitor the actions of the users. For pointing tasks, laser pointer is used as the pointing device. The first system is Vision-Based Keyboard System. In this system, the keyboard is a passive device. Therefore, it can be made up of any material having a keyboard layout image. The web camera is placed to see the entire keyboard image and captures the movement of the laser beam. The user enters a character to the computer by covering the corresponding character region in the keyboard layout image with the laser pointer. Additionally, this keyboard system can be easily adapted for disabled people who have little or no control of their hands to use a keyboard. The disabled user can attach a laser pointer to an eyeglass and control the beam of the laser pointer by only moving his/her head. For the same class of disabled people, Vision-Based Mouse System is also developed. By using the same setup used in the previous keyboard system, this system provides the users to control mouse cursor and actions. The last system is Vision-Based Continuous Graffiti1-like Text Entry System. The user sketches characters in a GraffitiTM-like alphabet in a continuous manner on a flat surface using a laser pointer. The beam of the laser pointer is tracked during the image sequences captured by a camera and the corresponding written word is recognized from the extracted trace of the laser beam.

Includes bibliography: p. 214-225 and index. / 232 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis investigates a computational model of vision based on assumptions pertaining to the physical structure of a camera and the scattering of light from visible surfaces. A sufficient condition to detect occlusions, intensity discontinuities, discontinuities in derivatives of intensity, surface discontinuities and discontinuities in derivatives of surfaces are given. This leads to an algorithm with linear time and space complexity to generate a collection of feature points with attributes in cyclically ordered groups. Two approaches to rejecting false hypotheses of correspondence were developed: an error minimising approach and an approach based on formal language. A non-iterative algorithm that can use the rotation between two cameras to produce an exact reconstruction of a scene is presented. Two methods of comparing global shapes with occlusions are pointed out: one based on a grammar, the other on Le's inequality on euclidean shapes. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998